Overview

Dataset statistics

Number of variables42
Number of observations25192
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.1 MiB
Average record size in memory336.0 B

Variable types

Numeric27
Categorical15

Alerts

num_outbound_cmds has constant value ""Constant
is_host_login has constant value ""Constant
service has a high cardinality: 66 distinct valuesHigh cardinality
src_bytes is highly overall correlated with dst_bytes and 10 other fieldsHigh correlation
dst_bytes is highly overall correlated with src_bytes and 8 other fieldsHigh correlation
hot is highly overall correlated with is_guest_loginHigh correlation
num_compromised is highly overall correlated with root_shell and 1 other fieldsHigh correlation
num_root is highly overall correlated with root_shell and 1 other fieldsHigh correlation
num_access_files is highly overall correlated with su_attemptedHigh correlation
count is highly overall correlated with src_bytes and 12 other fieldsHigh correlation
srv_count is highly overall correlated with count and 1 other fieldsHigh correlation
serror_rate is highly overall correlated with src_bytes and 10 other fieldsHigh correlation
srv_serror_rate is highly overall correlated with src_bytes and 9 other fieldsHigh correlation
rerror_rate is highly overall correlated with srv_rerror_rate and 2 other fieldsHigh correlation
srv_rerror_rate is highly overall correlated with rerror_rate and 2 other fieldsHigh correlation
same_srv_rate is highly overall correlated with src_bytes and 14 other fieldsHigh correlation
diff_srv_rate is highly overall correlated with src_bytes and 11 other fieldsHigh correlation
dst_host_count is highly overall correlated with count and 5 other fieldsHigh correlation
dst_host_srv_count is highly overall correlated with src_bytes and 9 other fieldsHigh correlation
dst_host_same_srv_rate is highly overall correlated with src_bytes and 13 other fieldsHigh correlation
dst_host_diff_srv_rate is highly overall correlated with src_bytes and 6 other fieldsHigh correlation
dst_host_same_src_port_rate is highly overall correlated with count and 3 other fieldsHigh correlation
dst_host_srv_diff_host_rate is highly overall correlated with count and 3 other fieldsHigh correlation
dst_host_serror_rate is highly overall correlated with src_bytes and 11 other fieldsHigh correlation
dst_host_srv_serror_rate is highly overall correlated with src_bytes and 8 other fieldsHigh correlation
dst_host_rerror_rate is highly overall correlated with rerror_rate and 2 other fieldsHigh correlation
dst_host_srv_rerror_rate is highly overall correlated with rerror_rate and 2 other fieldsHigh correlation
protocol_type is highly overall correlated with srv_count and 1 other fieldsHigh correlation
service is highly overall correlated with protocol_type and 3 other fieldsHigh correlation
flag is highly overall correlated with logged_in and 1 other fieldsHigh correlation
logged_in is highly overall correlated with count and 6 other fieldsHigh correlation
root_shell is highly overall correlated with num_compromised and 2 other fieldsHigh correlation
su_attempted is highly overall correlated with num_compromised and 3 other fieldsHigh correlation
is_guest_login is highly overall correlated with hot and 1 other fieldsHigh correlation
class is highly overall correlated with count and 10 other fieldsHigh correlation
flag is highly imbalanced (55.9%)Imbalance
land is highly imbalanced (99.9%)Imbalance
wrong_fragment is highly imbalanced (95.0%)Imbalance
urgent is highly imbalanced (99.9%)Imbalance
num_failed_logins is highly imbalanced (99.5%)Imbalance
root_shell is highly imbalanced (98.3%)Imbalance
su_attempted is highly imbalanced (99.3%)Imbalance
num_shells is highly imbalanced (99.5%)Imbalance
is_guest_login is highly imbalanced (92.5%)Imbalance
src_bytes is highly skewed (γ1 = 157.5585416)Skewed
dst_bytes is highly skewed (γ1 = 54.77757621)Skewed
num_compromised is highly skewed (γ1 = 62.19108752)Skewed
num_root is highly skewed (γ1 = 62.3210635)Skewed
num_file_creations is highly skewed (γ1 = 52.1416881)Skewed
num_access_files is highly skewed (γ1 = 41.75276352)Skewed
duration has 23168 (92.0%) zerosZeros
src_bytes has 9866 (39.2%) zerosZeros
dst_bytes has 13574 (53.9%) zerosZeros
hot has 24672 (97.9%) zerosZeros
num_compromised has 24920 (98.9%) zerosZeros
num_root has 25058 (99.5%) zerosZeros
num_file_creations has 25126 (99.7%) zerosZeros
num_access_files has 25113 (99.7%) zerosZeros
serror_rate has 17329 (68.8%) zerosZeros
srv_serror_rate has 17708 (70.3%) zerosZeros
rerror_rate has 21985 (87.3%) zerosZeros
srv_rerror_rate has 21959 (87.2%) zerosZeros
same_srv_rate has 543 (2.2%) zerosZeros
diff_srv_rate has 15245 (60.5%) zerosZeros
srv_diff_host_rate has 19517 (77.5%) zerosZeros
dst_host_same_srv_rate has 1379 (5.5%) zerosZeros
dst_host_diff_srv_rate has 9343 (37.1%) zerosZeros
dst_host_same_src_port_rate has 12673 (50.3%) zerosZeros
dst_host_srv_diff_host_rate has 17387 (69.0%) zerosZeros
dst_host_serror_rate has 16221 (64.4%) zerosZeros
dst_host_srv_serror_rate has 17005 (67.5%) zerosZeros
dst_host_rerror_rate has 20688 (82.1%) zerosZeros
dst_host_srv_rerror_rate has 21349 (84.7%) zerosZeros

Reproduction

Analysis started2023-04-20 00:18:26.205012
Analysis finished2023-04-20 00:21:07.524140
Duration2 minutes and 41.32 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

duration
Real number (ℝ)

Distinct758
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean305.0541
Minimum0
Maximum42862
Zeros23168
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:07.675037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum42862
Range42862
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2686.5556
Coefficient of variation (CV)8.8068169
Kurtosis146.70102
Mean305.0541
Median Absolute Deviation (MAD)0
Skewness11.532643
Sum7684923
Variance7217581.2
MonotonicityNot monotonic
2023-04-19T18:21:07.895946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23168
92.0%
1 374
 
1.5%
2 165
 
0.7%
3 102
 
0.4%
4 75
 
0.3%
5 62
 
0.2%
6 41
 
0.2%
27 40
 
0.2%
28 38
 
0.2%
7 26
 
0.1%
Other values (748) 1101
 
4.4%
ValueCountFrequency (%)
0 23168
92.0%
1 374
 
1.5%
2 165
 
0.7%
3 102
 
0.4%
4 75
 
0.3%
5 62
 
0.2%
6 41
 
0.2%
7 26
 
0.1%
8 19
 
0.1%
9 22
 
0.1%
ValueCountFrequency (%)
42862 1
< 0.1%
42658 1
< 0.1%
42636 1
< 0.1%
42470 1
< 0.1%
42260 1
< 0.1%
42021 1
< 0.1%
41802 1
< 0.1%
41561 1
< 0.1%
41541 1
< 0.1%
41476 1
< 0.1%

protocol_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
tcp
20526 
udp
3011 
icmp
 
1655

Length

Max length4
Median length3
Mean length3.0656955
Min length3

Characters and Unicode

Total characters77231
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtcp
2nd rowudp
3rd rowtcp
4th rowtcp
5th rowtcp

Common Values

ValueCountFrequency (%)
tcp 20526
81.5%
udp 3011
 
12.0%
icmp 1655
 
6.6%

Length

2023-04-19T18:21:08.092155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T18:21:08.309628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
tcp 20526
81.5%
udp 3011
 
12.0%
icmp 1655
 
6.6%

Most occurring characters

ValueCountFrequency (%)
p 25192
32.6%
c 22181
28.7%
t 20526
26.6%
u 3011
 
3.9%
d 3011
 
3.9%
i 1655
 
2.1%
m 1655
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 77231
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 25192
32.6%
c 22181
28.7%
t 20526
26.6%
u 3011
 
3.9%
d 3011
 
3.9%
i 1655
 
2.1%
m 1655
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 77231
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 25192
32.6%
c 22181
28.7%
t 20526
26.6%
u 3011
 
3.9%
d 3011
 
3.9%
i 1655
 
2.1%
m 1655
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77231
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 25192
32.6%
c 22181
28.7%
t 20526
26.6%
u 3011
 
3.9%
d 3011
 
3.9%
i 1655
 
2.1%
m 1655
 
2.1%

service
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct66
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
http
8003 
private
4351 
domain_u
1820 
smtp
1449 
ftp_data
1396 
Other values (61)
8173 

Length

Max length11
Median length10
Mean length5.4736821
Min length3

Characters and Unicode

Total characters137893
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowftp_data
2nd rowother
3rd rowprivate
4th rowhttp
5th rowhttp

Common Values

ValueCountFrequency (%)
http 8003
31.8%
private 4351
17.3%
domain_u 1820
 
7.2%
smtp 1449
 
5.8%
ftp_data 1396
 
5.5%
eco_i 909
 
3.6%
other 858
 
3.4%
ecr_i 613
 
2.4%
telnet 483
 
1.9%
finger 366
 
1.5%
Other values (56) 4944
19.6%

Length

2023-04-19T18:21:08.495866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
http 8003
31.8%
private 4351
17.3%
domain_u 1820
 
7.2%
smtp 1449
 
5.8%
ftp_data 1396
 
5.5%
eco_i 909
 
3.6%
other 858
 
3.4%
ecr_i 613
 
2.4%
telnet 483
 
1.9%
finger 366
 
1.5%
Other values (56) 4944
19.6%

Most occurring characters

ValueCountFrequency (%)
t 29049
21.1%
p 17578
12.7%
a 10319
 
7.5%
e 9860
 
7.2%
h 9852
 
7.1%
i 9744
 
7.1%
r 6989
 
5.1%
_ 5928
 
4.3%
o 4909
 
3.6%
n 4525
 
3.3%
Other values (29) 29140
21.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 130368
94.5%
Connector Punctuation 5928
 
4.3%
Decimal Number 1283
 
0.9%
Uppercase Letter 314
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 29049
22.3%
p 17578
13.5%
a 10319
 
7.9%
e 9860
 
7.6%
h 9852
 
7.6%
i 9744
 
7.5%
r 6989
 
5.4%
o 4909
 
3.8%
n 4525
 
3.5%
v 4458
 
3.4%
Other values (15) 23085
17.7%
Decimal Number
ValueCountFrequency (%)
4 364
28.4%
3 338
26.3%
0 174
13.6%
5 172
13.4%
9 172
13.4%
1 45
 
3.5%
2 17
 
1.3%
8 1
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
Z 172
54.8%
I 40
 
12.7%
R 40
 
12.7%
C 40
 
12.7%
X 22
 
7.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5928
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 130682
94.8%
Common 7211
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 29049
22.2%
p 17578
13.5%
a 10319
 
7.9%
e 9860
 
7.5%
h 9852
 
7.5%
i 9744
 
7.5%
r 6989
 
5.3%
o 4909
 
3.8%
n 4525
 
3.5%
v 4458
 
3.4%
Other values (20) 23399
17.9%
Common
ValueCountFrequency (%)
_ 5928
82.2%
4 364
 
5.0%
3 338
 
4.7%
0 174
 
2.4%
5 172
 
2.4%
9 172
 
2.4%
1 45
 
0.6%
2 17
 
0.2%
8 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 137893
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 29049
21.1%
p 17578
12.7%
a 10319
 
7.5%
e 9860
 
7.2%
h 9852
 
7.1%
i 9744
 
7.1%
r 6989
 
5.1%
_ 5928
 
4.3%
o 4909
 
3.6%
n 4525
 
3.3%
Other values (29) 29140
21.1%

flag
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
SF
14973 
S0
7009 
REJ
2216 
RSTR
 
497
RSTO
 
304
Other values (6)
 
193

Length

Max length6
Median length2
Mean length2.1550889
Min length2

Characters and Unicode

Total characters54291
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSF
2nd rowSF
3rd rowS0
4th rowSF
5th rowSF

Common Values

ValueCountFrequency (%)
SF 14973
59.4%
S0 7009
27.8%
REJ 2216
 
8.8%
RSTR 497
 
2.0%
RSTO 304
 
1.2%
S1 88
 
0.3%
SH 43
 
0.2%
RSTOS0 21
 
0.1%
S2 21
 
0.1%
S3 15
 
0.1%

Length

2023-04-19T18:21:08.760143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sf 14973
59.4%
s0 7009
27.8%
rej 2216
 
8.8%
rstr 497
 
2.0%
rsto 304
 
1.2%
s1 88
 
0.3%
sh 43
 
0.2%
rstos0 21
 
0.1%
s2 21
 
0.1%
s3 15
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S 22992
42.3%
F 14973
27.6%
0 7030
 
12.9%
R 3535
 
6.5%
E 2216
 
4.1%
J 2216
 
4.1%
T 827
 
1.5%
O 330
 
0.6%
1 88
 
0.2%
H 48
 
0.1%
Other values (2) 36
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 47137
86.8%
Decimal Number 7154
 
13.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 22992
48.8%
F 14973
31.8%
R 3535
 
7.5%
E 2216
 
4.7%
J 2216
 
4.7%
T 827
 
1.8%
O 330
 
0.7%
H 48
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 7030
98.3%
1 88
 
1.2%
2 21
 
0.3%
3 15
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 47137
86.8%
Common 7154
 
13.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 22992
48.8%
F 14973
31.8%
R 3535
 
7.5%
E 2216
 
4.7%
J 2216
 
4.7%
T 827
 
1.8%
O 330
 
0.7%
H 48
 
0.1%
Common
ValueCountFrequency (%)
0 7030
98.3%
1 88
 
1.2%
2 21
 
0.3%
3 15
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54291
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 22992
42.3%
F 14973
27.6%
0 7030
 
12.9%
R 3535
 
6.5%
E 2216
 
4.1%
J 2216
 
4.1%
T 827
 
1.5%
O 330
 
0.6%
1 88
 
0.2%
H 48
 
0.1%
Other values (2) 36
 
0.1%

src_bytes
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1665
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24330.628
Minimum0
Maximum3.8170909 × 108
Zeros9866
Zeros (%)39.2%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:09.009902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median44
Q3279
95-th percentile1486.45
Maximum3.8170909 × 108
Range3.8170909 × 108
Interquartile range (IQR)279

Descriptive statistics

Standard deviation2410805.4
Coefficient of variation (CV)99.08521
Kurtosis24944.614
Mean24330.628
Median Absolute Deviation (MAD)44
Skewness157.55854
Sum6.1293719 × 108
Variance5.8119827 × 1012
MonotonicityNot monotonic
2023-04-19T18:21:09.283766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9866
39.2%
8 738
 
2.9%
1 480
 
1.9%
44 467
 
1.9%
45 416
 
1.7%
1032 390
 
1.5%
46 284
 
1.1%
43 231
 
0.9%
147 210
 
0.8%
105 204
 
0.8%
Other values (1655) 11906
47.3%
ValueCountFrequency (%)
0 9866
39.2%
1 480
 
1.9%
4 1
 
< 0.1%
5 4
 
< 0.1%
6 32
 
0.1%
7 26
 
0.1%
8 738
 
2.9%
9 39
 
0.2%
10 32
 
0.1%
11 14
 
0.1%
ValueCountFrequency (%)
381709090 1
 
< 0.1%
7665876 1
 
< 0.1%
7248552 1
 
< 0.1%
5135678 3
 
< 0.1%
5133876 8
 
< 0.1%
5131424 2
 
< 0.1%
5097472 1
 
< 0.1%
2280318 1
 
< 0.1%
2194620 2
 
< 0.1%
2194619 44
0.2%

dst_bytes
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct3922
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3491.8472
Minimum0
Maximum5151385
Zeros13574
Zeros (%)53.9%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:09.521510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3530.25
95-th percentile8314
Maximum5151385
Range5151385
Interquartile range (IQR)530.25

Descriptive statistics

Standard deviation88830.718
Coefficient of variation (CV)25.439463
Kurtosis3130.1726
Mean3491.8472
Median Absolute Deviation (MAD)0
Skewness54.777576
Sum87966614
Variance7.8908965 × 109
MonotonicityNot monotonic
2023-04-19T18:21:09.749523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13574
53.9%
105 309
 
1.2%
8314 175
 
0.7%
44 115
 
0.5%
42 105
 
0.4%
330 105
 
0.4%
332 103
 
0.4%
331 97
 
0.4%
4 94
 
0.4%
329 88
 
0.3%
Other values (3912) 10427
41.4%
ValueCountFrequency (%)
0 13574
53.9%
1 6
 
< 0.1%
4 94
 
0.4%
15 7
 
< 0.1%
17 7
 
< 0.1%
18 3
 
< 0.1%
24 2
 
< 0.1%
26 3
 
< 0.1%
28 2
 
< 0.1%
29 8
 
< 0.1%
ValueCountFrequency (%)
5151385 1
< 0.1%
5150836 1
< 0.1%
5150772 1
< 0.1%
5150180 1
< 0.1%
5149533 1
< 0.1%
5131424 1
< 0.1%
5129964 1
< 0.1%
1639484 1
< 0.1%
1593580 1
< 0.1%
1437092 1
< 0.1%

land
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25190 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25192
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25190
> 99.9%
1 2
 
< 0.1%

Length

2023-04-19T18:21:09.950804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T18:21:10.129702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 25190
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 25190
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25190
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 25192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25190
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25190
> 99.9%
1 2
 
< 0.1%

wrong_fragment
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
24968 
3
 
187
1
 
37

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25192
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 24968
99.1%
3 187
 
0.7%
1 37
 
0.1%

Length

2023-04-19T18:21:10.283449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T18:21:10.502420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 24968
99.1%
3 187
 
0.7%
1 37
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 24968
99.1%
3 187
 
0.7%
1 37
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24968
99.1%
3 187
 
0.7%
1 37
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 25192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24968
99.1%
3 187
 
0.7%
1 37
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24968
99.1%
3 187
 
0.7%
1 37
 
0.1%

urgent
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25191 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25192
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25191
> 99.9%
1 1
 
< 0.1%

Length

2023-04-19T18:21:10.649523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T18:21:10.831324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 25191
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 25191
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25191
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 25192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25191
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25191
> 99.9%
1 1
 
< 0.1%

hot
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19803906
Minimum0
Maximum77
Zeros24672
Zeros (%)97.9%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:10.983885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum77
Range77
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1542018
Coefficient of variation (CV)10.877661
Kurtosis213.69796
Mean0.19803906
Median Absolute Deviation (MAD)0
Skewness13.589537
Sum4989
Variance4.6405853
MonotonicityNot monotonic
2023-04-19T18:21:11.184812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 24672
97.9%
2 200
 
0.8%
1 78
 
0.3%
30 55
 
0.2%
28 52
 
0.2%
4 37
 
0.1%
6 26
 
0.1%
5 17
 
0.1%
22 13
 
0.1%
24 9
 
< 0.1%
Other values (12) 33
 
0.1%
ValueCountFrequency (%)
0 24672
97.9%
1 78
 
0.3%
2 200
 
0.8%
3 7
 
< 0.1%
4 37
 
0.1%
5 17
 
0.1%
6 26
 
0.1%
7 2
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
77 1
 
< 0.1%
30 55
0.2%
28 52
0.2%
25 1
 
< 0.1%
24 9
 
< 0.1%
22 13
 
0.1%
20 1
 
< 0.1%
19 8
 
< 0.1%
18 6
 
< 0.1%
17 1
 
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25169 
1
 
19
2
 
2
3
 
1
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25192
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25169
99.9%
1 19
 
0.1%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Length

2023-04-19T18:21:11.378170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T18:21:11.568251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 25169
99.9%
1 19
 
0.1%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 25169
99.9%
1 19
 
0.1%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25169
99.9%
1 19
 
0.1%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 25192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25169
99.9%
1 19
 
0.1%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25169
99.9%
1 19
 
0.1%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

logged_in
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
15247 
1
9945 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25192
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 15247
60.5%
1 9945
39.5%

Length

2023-04-19T18:21:11.737703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T18:21:11.921831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15247
60.5%
1 9945
39.5%

Most occurring characters

ValueCountFrequency (%)
0 15247
60.5%
1 9945
39.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15247
60.5%
1 9945
39.5%

Most occurring scripts

ValueCountFrequency (%)
Common 25192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15247
60.5%
1 9945
39.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15247
60.5%
1 9945
39.5%

num_compromised
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22785011
Minimum0
Maximum884
Zeros24920
Zeros (%)98.9%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:12.080639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum884
Range884
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.417352
Coefficient of variation (CV)45.720197
Kurtosis4313.7758
Mean0.22785011
Median Absolute Deviation (MAD)0
Skewness62.191088
Sum5740
Variance108.52122
MonotonicityNot monotonic
2023-04-19T18:21:12.277766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 24920
98.9%
1 194
 
0.8%
2 21
 
0.1%
4 13
 
0.1%
6 8
 
< 0.1%
3 7
 
< 0.1%
5 5
 
< 0.1%
7 2
 
< 0.1%
151 2
 
< 0.1%
12 2
 
< 0.1%
Other values (18) 18
 
0.1%
ValueCountFrequency (%)
0 24920
98.9%
1 194
 
0.8%
2 21
 
0.1%
3 7
 
< 0.1%
4 13
 
0.1%
5 5
 
< 0.1%
6 8
 
< 0.1%
7 2
 
< 0.1%
9 1
 
< 0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
884 1
< 0.1%
789 1
< 0.1%
558 1
< 0.1%
520 1
< 0.1%
462 1
< 0.1%
457 1
< 0.1%
371 1
< 0.1%
217 1
< 0.1%
193 1
< 0.1%
157 1
< 0.1%

root_shell
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25153 
1
 
39

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25192
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25153
99.8%
1 39
 
0.2%

Length

2023-04-19T18:21:12.458428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T18:21:12.635107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 25153
99.8%
1 39
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 25153
99.8%
1 39
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25153
99.8%
1 39
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 25192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25153
99.8%
1 39
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25153
99.8%
1 39
 
0.2%

su_attempted
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25171 
2
 
13
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25192
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25171
99.9%
2 13
 
0.1%
1 8
 
< 0.1%

Length

2023-04-19T18:21:12.778153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T18:21:12.958169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 25171
99.9%
2 13
 
0.1%
1 8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 25171
99.9%
2 13
 
0.1%
1 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25171
99.9%
2 13
 
0.1%
1 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 25192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25171
99.9%
2 13
 
0.1%
1 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25171
99.9%
2 13
 
0.1%
1 8
 
< 0.1%

num_root
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24984122
Minimum0
Maximum975
Zeros25058
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:13.115366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum975
Range975
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.500842
Coefficient of variation (CV)46.032604
Kurtosis4315.7675
Mean0.24984122
Median Absolute Deviation (MAD)0
Skewness62.321064
Sum6294
Variance132.26936
MonotonicityNot monotonic
2023-04-19T18:21:13.305843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 25058
99.5%
1 47
 
0.2%
9 24
 
0.1%
6 23
 
0.1%
2 10
 
< 0.1%
5 6
 
< 0.1%
4 2
 
< 0.1%
3 2
 
< 0.1%
14 1
 
< 0.1%
100 1
 
< 0.1%
Other values (18) 18
 
0.1%
ValueCountFrequency (%)
0 25058
99.5%
1 47
 
0.2%
2 10
 
< 0.1%
3 2
 
< 0.1%
4 2
 
< 0.1%
5 6
 
< 0.1%
6 23
 
0.1%
7 1
 
< 0.1%
9 24
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
975 1
< 0.1%
867 1
< 0.1%
629 1
< 0.1%
572 1
< 0.1%
512 1
< 0.1%
508 1
< 0.1%
417 1
< 0.1%
247 1
< 0.1%
191 1
< 0.1%
179 1
< 0.1%

num_file_creations
Real number (ℝ)

SKEWED  ZEROS 

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.014726897
Minimum0
Maximum40
Zeros25126
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:13.498421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum40
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5296023
Coefficient of variation (CV)35.961566
Kurtosis3158.2051
Mean0.014726897
Median Absolute Deviation (MAD)0
Skewness52.141688
Sum371
Variance0.2804786
MonotonicityNot monotonic
2023-04-19T18:21:13.687409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 25126
99.7%
1 37
 
0.1%
2 7
 
< 0.1%
4 3
 
< 0.1%
8 2
 
< 0.1%
18 2
 
< 0.1%
5 2
 
< 0.1%
21 1
 
< 0.1%
11 1
 
< 0.1%
20 1
 
< 0.1%
Other values (10) 10
 
< 0.1%
ValueCountFrequency (%)
0 25126
99.7%
1 37
 
0.1%
2 7
 
< 0.1%
3 1
 
< 0.1%
4 3
 
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
8 2
 
< 0.1%
11 1
 
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
40 1
< 0.1%
38 1
< 0.1%
29 1
< 0.1%
21 1
< 0.1%
20 1
< 0.1%
19 1
< 0.1%
18 2
< 0.1%
17 1
< 0.1%
15 1
< 0.1%
14 1
< 0.1%

num_shells
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25183 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25192
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25183
> 99.9%
1 9
 
< 0.1%

Length

2023-04-19T18:21:13.883583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T18:21:14.093571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 25183
> 99.9%
1 9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 25183
> 99.9%
1 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25183
> 99.9%
1 9
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 25192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25183
> 99.9%
1 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25183
> 99.9%
1 9
 
< 0.1%

num_access_files
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0043267704
Minimum0
Maximum8
Zeros25113
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:15.509089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.098523977
Coefficient of variation (CV)22.770789
Kurtosis2499.9078
Mean0.0043267704
Median Absolute Deviation (MAD)0
Skewness41.752764
Sum109
Variance0.009706974
MonotonicityNot monotonic
2023-04-19T18:21:15.705380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 25113
99.7%
1 65
 
0.3%
2 8
 
< 0.1%
3 2
 
< 0.1%
5 2
 
< 0.1%
4 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 25113
99.7%
1 65
 
0.3%
2 8
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
5 2
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
5 2
 
< 0.1%
4 1
 
< 0.1%
3 2
 
< 0.1%
2 8
 
< 0.1%
1 65
 
0.3%
0 25113
99.7%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25192 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25192
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25192
100.0%

Length

2023-04-19T18:21:15.922687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T18:21:16.113254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 25192
100.0%

Most occurring characters

ValueCountFrequency (%)
0 25192
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25192
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25192
100.0%

is_host_login
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25192 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25192
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25192
100.0%

Length

2023-04-19T18:21:16.323334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T18:21:16.512604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 25192
100.0%

Most occurring characters

ValueCountFrequency (%)
0 25192
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25192
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25192
100.0%

is_guest_login
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
24962 
1
 
230

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25192
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 24962
99.1%
1 230
 
0.9%

Length

2023-04-19T18:21:16.671285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T18:21:16.868738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 24962
99.1%
1 230
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 24962
99.1%
1 230
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24962
99.1%
1 230
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 25192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24962
99.1%
1 230
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24962
99.1%
1 230
 
0.9%

count
Real number (ℝ)

Distinct466
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.59118
Minimum1
Maximum511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:17.092314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median14
Q3144
95-th percentile286
Maximum511
Range510
Interquartile range (IQR)142

Descriptive statistics

Standard deviation114.67345
Coefficient of variation (CV)1.3556195
Kurtosis1.978018
Mean84.59118
Median Absolute Deviation (MAD)13
Skewness1.5037325
Sum2131021
Variance13150
MonotonicityNot monotonic
2023-04-19T18:21:17.482518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5519
21.9%
2 1934
 
7.7%
3 769
 
3.1%
4 696
 
2.8%
5 597
 
2.4%
6 477
 
1.9%
7 462
 
1.8%
8 404
 
1.6%
9 342
 
1.4%
11 317
 
1.3%
Other values (456) 13675
54.3%
ValueCountFrequency (%)
1 5519
21.9%
2 1934
 
7.7%
3 769
 
3.1%
4 696
 
2.8%
5 597
 
2.4%
6 477
 
1.9%
7 462
 
1.8%
8 404
 
1.6%
9 342
 
1.4%
10 311
 
1.2%
ValueCountFrequency (%)
511 293
1.2%
510 58
 
0.2%
509 49
 
0.2%
508 6
 
< 0.1%
507 1
 
< 0.1%
506 1
 
< 0.1%
502 1
 
< 0.1%
500 3
 
< 0.1%
497 1
 
< 0.1%
496 2
 
< 0.1%

srv_count
Real number (ℝ)

Distinct414
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.698754
Minimum1
Maximum511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:17.804353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median8
Q318
95-th percentile157
Maximum511
Range510
Interquartile range (IQR)16

Descriptive statistics

Standard deviation72.468242
Coefficient of variation (CV)2.6162997
Kurtosis24.396696
Mean27.698754
Median Absolute Deviation (MAD)7
Skewness4.7075228
Sum697787
Variance5251.6461
MonotonicityNot monotonic
2023-04-19T18:21:18.059143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5080
20.2%
2 2538
 
10.1%
3 1223
 
4.9%
4 1086
 
4.3%
5 913
 
3.6%
6 849
 
3.4%
7 800
 
3.2%
8 751
 
3.0%
9 718
 
2.9%
11 688
 
2.7%
Other values (404) 10546
41.9%
ValueCountFrequency (%)
1 5080
20.2%
2 2538
10.1%
3 1223
 
4.9%
4 1086
 
4.3%
5 913
 
3.6%
6 849
 
3.4%
7 800
 
3.2%
8 751
 
3.0%
9 718
 
2.9%
10 647
 
2.6%
ValueCountFrequency (%)
511 200
0.8%
510 36
 
0.1%
509 6
 
< 0.1%
508 2
 
< 0.1%
500 1
 
< 0.1%
497 1
 
< 0.1%
496 1
 
< 0.1%
492 2
 
< 0.1%
489 1
 
< 0.1%
488 1
 
< 0.1%

serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28633773
Minimum0
Maximum1
Zeros17329
Zeros (%)68.8%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:18.307766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.44731232
Coefficient of variation (CV)1.5621844
Kurtosis-1.0743895
Mean0.28633773
Median Absolute Deviation (MAD)0
Skewness0.95264674
Sum7213.42
Variance0.20008831
MonotonicityNot monotonic
2023-04-19T18:21:18.556110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17329
68.8%
1 6941
27.6%
0.5 122
 
0.5%
0.07 53
 
0.2%
0.06 50
 
0.2%
0.05 50
 
0.2%
0.33 48
 
0.2%
0.01 46
 
0.2%
0.08 46
 
0.2%
0.25 43
 
0.2%
Other values (60) 464
 
1.8%
ValueCountFrequency (%)
0 17329
68.8%
0.01 46
 
0.2%
0.02 16
 
0.1%
0.03 31
 
0.1%
0.04 29
 
0.1%
0.05 50
 
0.2%
0.06 50
 
0.2%
0.07 53
 
0.2%
0.08 46
 
0.2%
0.09 38
 
0.2%
ValueCountFrequency (%)
1 6941
27.6%
0.99 41
 
0.2%
0.98 12
 
< 0.1%
0.97 16
 
0.1%
0.96 7
 
< 0.1%
0.95 6
 
< 0.1%
0.94 2
 
< 0.1%
0.93 6
 
< 0.1%
0.92 2
 
< 0.1%
0.91 1
 
< 0.1%

srv_serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28376231
Minimum0
Maximum1
Zeros17708
Zeros (%)70.3%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:18.802637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.44759891
Coefficient of variation (CV)1.5773727
Kurtosis-1.0578876
Mean0.28376231
Median Absolute Deviation (MAD)0
Skewness0.96349972
Sum7148.54
Variance0.20034478
MonotonicityNot monotonic
2023-04-19T18:21:19.040448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17708
70.3%
1 7003
 
27.8%
0.5 94
 
0.4%
0.33 51
 
0.2%
0.25 42
 
0.2%
0.2 32
 
0.1%
0.05 26
 
0.1%
0.17 22
 
0.1%
0.03 20
 
0.1%
0.04 20
 
0.1%
Other values (46) 174
 
0.7%
ValueCountFrequency (%)
0 17708
70.3%
0.01 1
 
< 0.1%
0.02 13
 
0.1%
0.03 20
 
0.1%
0.04 20
 
0.1%
0.05 26
 
0.1%
0.06 10
 
< 0.1%
0.07 16
 
0.1%
0.08 10
 
< 0.1%
0.09 10
 
< 0.1%
ValueCountFrequency (%)
1 7003
27.8%
0.95 9
 
< 0.1%
0.94 1
 
< 0.1%
0.93 1
 
< 0.1%
0.92 3
 
< 0.1%
0.91 3
 
< 0.1%
0.9 4
 
< 0.1%
0.89 3
 
< 0.1%
0.88 1
 
< 0.1%
0.86 1
 
< 0.1%

rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11863012
Minimum0
Maximum1
Zeros21985
Zeros (%)87.3%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:19.283239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.31874547
Coefficient of variation (CV)2.6868848
Kurtosis3.547199
Mean0.11863012
Median Absolute Deviation (MAD)0
Skewness2.3463583
Sum2988.53
Variance0.10159868
MonotonicityNot monotonic
2023-04-19T18:21:19.516953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21985
87.3%
1 2552
 
10.1%
0.9 43
 
0.2%
0.89 39
 
0.2%
0.91 38
 
0.2%
0.92 38
 
0.2%
0.95 37
 
0.1%
0.5 36
 
0.1%
0.93 34
 
0.1%
0.94 31
 
0.1%
Other values (62) 359
 
1.4%
ValueCountFrequency (%)
0 21985
87.3%
0.01 8
 
< 0.1%
0.02 15
 
0.1%
0.03 21
 
0.1%
0.04 9
 
< 0.1%
0.05 8
 
< 0.1%
0.06 2
 
< 0.1%
0.07 8
 
< 0.1%
0.08 5
 
< 0.1%
0.09 1
 
< 0.1%
ValueCountFrequency (%)
1 2552
10.1%
0.99 6
 
< 0.1%
0.98 2
 
< 0.1%
0.97 7
 
< 0.1%
0.96 11
 
< 0.1%
0.95 37
 
0.1%
0.94 31
 
0.1%
0.93 34
 
0.1%
0.92 38
 
0.2%
0.91 38
 
0.2%

srv_rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1202604
Minimum0
Maximum1
Zeros21959
Zeros (%)87.2%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:19.733411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.32233535
Coefficient of variation (CV)2.6803117
Kurtosis3.5138754
Mean0.1202604
Median Absolute Deviation (MAD)0
Skewness2.340787
Sum3029.6
Variance0.10390008
MonotonicityNot monotonic
2023-04-19T18:21:19.938241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 21959
87.2%
1 2937
 
11.7%
0.5 56
 
0.2%
0.33 32
 
0.1%
0.25 26
 
0.1%
0.17 17
 
0.1%
0.2 16
 
0.1%
0.04 14
 
0.1%
0.08 11
 
< 0.1%
0.67 11
 
< 0.1%
Other values (32) 113
 
0.4%
ValueCountFrequency (%)
0 21959
87.2%
0.02 9
 
< 0.1%
0.03 8
 
< 0.1%
0.04 14
 
0.1%
0.05 8
 
< 0.1%
0.06 10
 
< 0.1%
0.07 7
 
< 0.1%
0.08 11
 
< 0.1%
0.09 2
 
< 0.1%
0.1 8
 
< 0.1%
ValueCountFrequency (%)
1 2937
11.7%
0.85 1
 
< 0.1%
0.84 1
 
< 0.1%
0.83 3
 
< 0.1%
0.81 2
 
< 0.1%
0.8 3
 
< 0.1%
0.79 2
 
< 0.1%
0.76 1
 
< 0.1%
0.75 5
 
< 0.1%
0.74 1
 
< 0.1%

same_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct97
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.66055891
Minimum0
Maximum1
Zeros543
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:20.154259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.09
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.91

Descriptive statistics

Standard deviation0.43963738
Coefficient of variation (CV)0.66555363
Kurtosis-1.6117348
Mean0.66055891
Median Absolute Deviation (MAD)0
Skewness-0.57048925
Sum16640.8
Variance0.19328103
MonotonicityNot monotonic
2023-04-19T18:21:20.388627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 15357
61.0%
0.01 827
 
3.3%
0.02 710
 
2.8%
0.06 681
 
2.7%
0.03 678
 
2.7%
0.07 671
 
2.7%
0.04 658
 
2.6%
0.08 601
 
2.4%
0.05 590
 
2.3%
0 543
 
2.2%
Other values (87) 3876
 
15.4%
ValueCountFrequency (%)
0 543
2.2%
0.01 827
3.3%
0.02 710
2.8%
0.03 678
2.7%
0.04 658
2.6%
0.05 590
2.3%
0.06 681
2.7%
0.07 671
2.7%
0.08 601
2.4%
0.09 399
1.6%
ValueCountFrequency (%)
1 15357
61.0%
0.99 147
 
0.6%
0.98 18
 
0.1%
0.97 9
 
< 0.1%
0.96 4
 
< 0.1%
0.95 3
 
< 0.1%
0.94 3
 
< 0.1%
0.93 9
 
< 0.1%
0.92 6
 
< 0.1%
0.91 2
 
< 0.1%

diff_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct79
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.062363052
Minimum0
Maximum1
Zeros15245
Zeros (%)60.5%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:20.615044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.06
95-th percentile0.29
Maximum1
Range1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.17855
Coefficient of variation (CV)2.8630734
Kurtosis19.295833
Mean0.062363052
Median Absolute Deviation (MAD)0
Skewness4.4177491
Sum1571.05
Variance0.031880101
MonotonicityNot monotonic
2023-04-19T18:21:20.843676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15245
60.5%
0.06 3861
 
15.3%
0.07 1947
 
7.7%
0.05 1350
 
5.4%
1 663
 
2.6%
0.08 374
 
1.5%
0.01 194
 
0.8%
0.04 130
 
0.5%
0.09 121
 
0.5%
0.5 116
 
0.5%
Other values (69) 1191
 
4.7%
ValueCountFrequency (%)
0 15245
60.5%
0.01 194
 
0.8%
0.02 49
 
0.2%
0.03 48
 
0.2%
0.04 130
 
0.5%
0.05 1350
 
5.4%
0.06 3861
 
15.3%
0.07 1947
 
7.7%
0.08 374
 
1.5%
0.09 121
 
0.5%
ValueCountFrequency (%)
1 663
2.6%
0.99 10
 
< 0.1%
0.98 2
 
< 0.1%
0.97 1
 
< 0.1%
0.96 8
 
< 0.1%
0.95 12
 
< 0.1%
0.83 2
 
< 0.1%
0.82 1
 
< 0.1%
0.8 2
 
< 0.1%
0.79 1
 
< 0.1%

srv_diff_host_rate
Real number (ℝ)

Distinct57
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.095930851
Minimum0
Maximum1
Zeros19517
Zeros (%)77.5%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:21.131619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.25658285
Coefficient of variation (CV)2.6746646
Kurtosis7.0101581
Mean0.095930851
Median Absolute Deviation (MAD)0
Skewness2.8859421
Sum2416.69
Variance0.065834757
MonotonicityNot monotonic
2023-04-19T18:21:21.398014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19517
77.5%
1 1559
 
6.2%
0.01 586
 
2.3%
0.67 210
 
0.8%
0.5 193
 
0.8%
0.12 170
 
0.7%
0.33 167
 
0.7%
0.25 164
 
0.7%
0.02 153
 
0.6%
0.11 143
 
0.6%
Other values (47) 2330
 
9.2%
ValueCountFrequency (%)
0 19517
77.5%
0.01 586
 
2.3%
0.02 153
 
0.6%
0.03 40
 
0.2%
0.04 39
 
0.2%
0.05 59
 
0.2%
0.06 118
 
0.5%
0.07 111
 
0.4%
0.08 124
 
0.5%
0.09 112
 
0.4%
ValueCountFrequency (%)
1 1559
6.2%
0.88 1
 
< 0.1%
0.83 1
 
< 0.1%
0.8 14
 
0.1%
0.75 58
 
0.2%
0.71 3
 
< 0.1%
0.67 210
 
0.8%
0.62 3
 
< 0.1%
0.6 41
 
0.2%
0.57 7
 
< 0.1%

dst_host_count
Real number (ℝ)

Distinct256
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.53207
Minimum0
Maximum255
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:21.638493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q184
median255
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)171

Descriptive statistics

Standard deviation98.993895
Coefficient of variation (CV)0.54233699
Kurtosis-1.0447809
Mean182.53207
Median Absolute Deviation (MAD)0
Skewness-0.84316081
Sum4598348
Variance9799.7913
MonotonicityNot monotonic
2023-04-19T18:21:21.907949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 14850
58.9%
1 601
 
2.4%
2 554
 
2.2%
3 251
 
1.0%
4 241
 
1.0%
5 162
 
0.6%
6 157
 
0.6%
8 134
 
0.5%
10 112
 
0.4%
11 111
 
0.4%
Other values (246) 8019
31.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 601
2.4%
2 554
2.2%
3 251
1.0%
4 241
1.0%
5 162
 
0.6%
6 157
 
0.6%
7 107
 
0.4%
8 134
 
0.5%
9 110
 
0.4%
ValueCountFrequency (%)
255 14850
58.9%
254 17
 
0.1%
253 17
 
0.1%
252 17
 
0.1%
251 12
 
< 0.1%
250 18
 
0.1%
249 15
 
0.1%
248 17
 
0.1%
247 21
 
0.1%
246 23
 
0.1%

dst_host_srv_count
Real number (ℝ)

Distinct256
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.06304
Minimum0
Maximum255
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:22.165978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median61
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)245

Descriptive statistics

Standard deviation110.64685
Coefficient of variation (CV)0.96161942
Kurtosis-1.75104
Mean115.06304
Median Absolute Deviation (MAD)59
Skewness0.29430576
Sum2898668
Variance12242.725
MonotonicityNot monotonic
2023-04-19T18:21:22.428217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 7148
28.4%
1 1658
 
6.6%
2 1041
 
4.1%
3 556
 
2.2%
4 516
 
2.0%
20 475
 
1.9%
5 464
 
1.8%
6 453
 
1.8%
254 440
 
1.7%
19 433
 
1.7%
Other values (246) 12008
47.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1658
6.6%
2 1041
4.1%
3 556
 
2.2%
4 516
 
2.0%
5 464
 
1.8%
6 453
 
1.8%
7 410
 
1.6%
8 426
 
1.7%
9 404
 
1.6%
ValueCountFrequency (%)
255 7148
28.4%
254 440
 
1.7%
253 91
 
0.4%
252 35
 
0.1%
251 81
 
0.3%
250 67
 
0.3%
249 42
 
0.2%
248 44
 
0.2%
247 57
 
0.2%
246 52
 
0.2%

dst_host_same_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51979081
Minimum0
Maximum1
Zeros1379
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:22.700130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.05
median0.51
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation0.44894392
Coefficient of variation (CV)0.86370115
Kurtosis-1.8846326
Mean0.51979081
Median Absolute Deviation (MAD)0.49
Skewness-0.0040236622
Sum13094.57
Variance0.20155064
MonotonicityNot monotonic
2023-04-19T18:21:22.952532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 9758
38.7%
0.01 1541
 
6.1%
0 1379
 
5.5%
0.02 1325
 
5.3%
0.07 1122
 
4.5%
0.04 1046
 
4.2%
0.05 1017
 
4.0%
0.03 799
 
3.2%
0.06 701
 
2.8%
0.08 577
 
2.3%
Other values (91) 5927
23.5%
ValueCountFrequency (%)
0 1379
5.5%
0.01 1541
6.1%
0.02 1325
5.3%
0.03 799
3.2%
0.04 1046
4.2%
0.05 1017
4.0%
0.06 701
2.8%
0.07 1122
4.5%
0.08 577
 
2.3%
0.09 360
 
1.4%
ValueCountFrequency (%)
1 9758
38.7%
0.99 121
 
0.5%
0.98 170
 
0.7%
0.97 101
 
0.4%
0.96 160
 
0.6%
0.95 126
 
0.5%
0.94 87
 
0.3%
0.93 81
 
0.3%
0.92 72
 
0.3%
0.91 69
 
0.3%

dst_host_diff_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.082538504
Minimum0
Maximum1
Zeros9343
Zeros (%)37.1%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:23.202556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.03
Q30.07
95-th percentile0.56
Maximum1
Range1
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.18719111
Coefficient of variation (CV)2.2679247
Kurtosis12.727509
Mean0.082538504
Median Absolute Deviation (MAD)0.03
Skewness3.616185
Sum2079.31
Variance0.035040513
MonotonicityNot monotonic
2023-04-19T18:21:23.521821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9343
37.1%
0.07 3448
 
13.7%
0.06 1917
 
7.6%
0.01 1881
 
7.5%
0.05 1436
 
5.7%
0.08 1367
 
5.4%
0.02 1327
 
5.3%
0.03 745
 
3.0%
0.04 603
 
2.4%
0.09 523
 
2.1%
Other values (91) 2602
 
10.3%
ValueCountFrequency (%)
0 9343
37.1%
0.01 1881
 
7.5%
0.02 1327
 
5.3%
0.03 745
 
3.0%
0.04 603
 
2.4%
0.05 1436
 
5.7%
0.06 1917
 
7.6%
0.07 3448
 
13.7%
0.08 1367
 
5.4%
0.09 523
 
2.1%
ValueCountFrequency (%)
1 408
1.6%
0.99 7
 
< 0.1%
0.98 6
 
< 0.1%
0.97 18
 
0.1%
0.96 12
 
< 0.1%
0.95 14
 
0.1%
0.94 12
 
< 0.1%
0.93 3
 
< 0.1%
0.92 6
 
< 0.1%
0.91 28
 
0.1%

dst_host_same_src_port_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14745276
Minimum0
Maximum1
Zeros12673
Zeros (%)50.3%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:23.907392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.06
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.30836659
Coefficient of variation (CV)2.0912907
Kurtosis2.8108033
Mean0.14745276
Median Absolute Deviation (MAD)0
Skewness2.0985268
Sum3714.63
Variance0.095089954
MonotonicityNot monotonic
2023-04-19T18:21:24.227660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12673
50.3%
0.01 3557
 
14.1%
1 2052
 
8.1%
0.02 1115
 
4.4%
0.03 624
 
2.5%
0.04 447
 
1.8%
0.05 315
 
1.3%
0.5 232
 
0.9%
0.08 230
 
0.9%
0.06 226
 
0.9%
Other values (91) 3721
 
14.8%
ValueCountFrequency (%)
0 12673
50.3%
0.01 3557
 
14.1%
0.02 1115
 
4.4%
0.03 624
 
2.5%
0.04 447
 
1.8%
0.05 315
 
1.3%
0.06 226
 
0.9%
0.07 199
 
0.8%
0.08 230
 
0.9%
0.09 150
 
0.6%
ValueCountFrequency (%)
1 2052
8.1%
0.99 19
 
0.1%
0.98 37
 
0.1%
0.97 32
 
0.1%
0.96 46
 
0.2%
0.95 61
 
0.2%
0.94 24
 
0.1%
0.93 33
 
0.1%
0.92 15
 
0.1%
0.91 29
 
0.1%

dst_host_srv_diff_host_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.031844236
Minimum0
Maximum1
Zeros17387
Zeros (%)69.0%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:24.471310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.02
95-th percentile0.18
Maximum1
Range1
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.11057497
Coefficient of variation (CV)3.4723699
Kurtosis36.8991
Mean0.031844236
Median Absolute Deviation (MAD)0
Skewness5.6170652
Sum802.22
Variance0.012226824
MonotonicityNot monotonic
2023-04-19T18:21:24.729732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17387
69.0%
0.02 1612
 
6.4%
0.01 1468
 
5.8%
0.03 950
 
3.8%
0.04 870
 
3.5%
0.05 608
 
2.4%
0.5 317
 
1.3%
0.06 264
 
1.0%
0.07 215
 
0.9%
0.25 205
 
0.8%
Other values (53) 1296
 
5.1%
ValueCountFrequency (%)
0 17387
69.0%
0.01 1468
 
5.8%
0.02 1612
 
6.4%
0.03 950
 
3.8%
0.04 870
 
3.5%
0.05 608
 
2.4%
0.06 264
 
1.0%
0.07 215
 
0.9%
0.08 87
 
0.3%
0.09 71
 
0.3%
ValueCountFrequency (%)
1 132
0.5%
0.97 1
 
< 0.1%
0.86 1
 
< 0.1%
0.8 1
 
< 0.1%
0.75 3
 
< 0.1%
0.67 16
 
0.1%
0.6 5
 
< 0.1%
0.57 5
 
< 0.1%
0.56 8
 
< 0.1%
0.55 4
 
< 0.1%

dst_host_serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct100
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28580025
Minimum0
Maximum1
Zeros16221
Zeros (%)64.4%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:24.986955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.44531648
Coefficient of variation (CV)1.5581388
Kurtosis-1.0613589
Mean0.28580025
Median Absolute Deviation (MAD)0
Skewness0.95814722
Sum7199.88
Variance0.19830676
MonotonicityNot monotonic
2023-04-19T18:21:25.231506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16221
64.4%
1 6739
26.8%
0.01 680
 
2.7%
0.02 237
 
0.9%
0.03 149
 
0.6%
0.04 83
 
0.3%
0.09 79
 
0.3%
0.08 69
 
0.3%
0.05 64
 
0.3%
0.99 59
 
0.2%
Other values (90) 812
 
3.2%
ValueCountFrequency (%)
0 16221
64.4%
0.01 680
 
2.7%
0.02 237
 
0.9%
0.03 149
 
0.6%
0.04 83
 
0.3%
0.05 64
 
0.3%
0.06 34
 
0.1%
0.07 47
 
0.2%
0.08 69
 
0.3%
0.09 79
 
0.3%
ValueCountFrequency (%)
1 6739
26.8%
0.99 59
 
0.2%
0.98 36
 
0.1%
0.97 18
 
0.1%
0.96 20
 
0.1%
0.95 14
 
0.1%
0.94 21
 
0.1%
0.93 15
 
0.1%
0.92 11
 
< 0.1%
0.91 8
 
< 0.1%

dst_host_srv_serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct88
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27984638
Minimum0
Maximum1
Zeros17005
Zeros (%)67.5%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:25.485282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.44607533
Coefficient of variation (CV)1.5940007
Kurtosis-1.0217227
Mean0.27984638
Median Absolute Deviation (MAD)0
Skewness0.98433873
Sum7049.89
Variance0.1989832
MonotonicityNot monotonic
2023-04-19T18:21:25.742305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17005
67.5%
1 6862
27.2%
0.01 758
 
3.0%
0.02 136
 
0.5%
0.03 32
 
0.1%
0.5 24
 
0.1%
0.08 16
 
0.1%
0.12 16
 
0.1%
0.04 15
 
0.1%
0.05 15
 
0.1%
Other values (78) 313
 
1.2%
ValueCountFrequency (%)
0 17005
67.5%
0.01 758
 
3.0%
0.02 136
 
0.5%
0.03 32
 
0.1%
0.04 15
 
0.1%
0.05 15
 
0.1%
0.06 15
 
0.1%
0.07 14
 
0.1%
0.08 16
 
0.1%
0.09 10
 
< 0.1%
ValueCountFrequency (%)
1 6862
27.2%
0.98 6
 
< 0.1%
0.97 13
 
0.1%
0.96 13
 
0.1%
0.95 6
 
< 0.1%
0.94 8
 
< 0.1%
0.93 7
 
< 0.1%
0.92 4
 
< 0.1%
0.91 9
 
< 0.1%
0.9 2
 
< 0.1%

dst_host_rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1178001
Minimum0
Maximum1
Zeros20688
Zeros (%)82.1%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:25.992416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.30586925
Coefficient of variation (CV)2.596511
Kurtosis3.7655958
Mean0.1178001
Median Absolute Deviation (MAD)0
Skewness2.3637067
Sum2967.62
Variance0.093555995
MonotonicityNot monotonic
2023-04-19T18:21:26.230517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20688
82.1%
1 2069
 
8.2%
0.01 359
 
1.4%
0.02 232
 
0.9%
0.03 110
 
0.4%
0.05 85
 
0.3%
0.04 77
 
0.3%
0.92 52
 
0.2%
0.9 46
 
0.2%
0.91 43
 
0.2%
Other values (91) 1431
 
5.7%
ValueCountFrequency (%)
0 20688
82.1%
0.01 359
 
1.4%
0.02 232
 
0.9%
0.03 110
 
0.4%
0.04 77
 
0.3%
0.05 85
 
0.3%
0.06 33
 
0.1%
0.07 39
 
0.2%
0.08 37
 
0.1%
0.09 15
 
0.1%
ValueCountFrequency (%)
1 2069
8.2%
0.99 7
 
< 0.1%
0.98 12
 
< 0.1%
0.97 20
 
0.1%
0.96 39
 
0.2%
0.95 29
 
0.1%
0.94 23
 
0.1%
0.93 23
 
0.1%
0.92 52
 
0.2%
0.91 43
 
0.2%

dst_host_srv_rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct100
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11876945
Minimum0
Maximum1
Zeros21349
Zeros (%)84.7%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2023-04-19T18:21:26.466677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.31733347
Coefficient of variation (CV)2.6718442
Kurtosis3.633093
Mean0.11876945
Median Absolute Deviation (MAD)0
Skewness2.3604836
Sum2992.04
Variance0.10070053
MonotonicityNot monotonic
2023-04-19T18:21:26.697845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21349
84.7%
1 2617
 
10.4%
0.01 253
 
1.0%
0.02 124
 
0.5%
0.03 78
 
0.3%
0.04 69
 
0.3%
0.05 63
 
0.3%
0.06 36
 
0.1%
0.99 33
 
0.1%
0.98 30
 
0.1%
Other values (90) 540
 
2.1%
ValueCountFrequency (%)
0 21349
84.7%
0.01 253
 
1.0%
0.02 124
 
0.5%
0.03 78
 
0.3%
0.04 69
 
0.3%
0.05 63
 
0.3%
0.06 36
 
0.1%
0.07 17
 
0.1%
0.08 18
 
0.1%
0.09 11
 
< 0.1%
ValueCountFrequency (%)
1 2617
10.4%
0.99 33
 
0.1%
0.98 30
 
0.1%
0.97 19
 
0.1%
0.96 17
 
0.1%
0.95 11
 
< 0.1%
0.94 11
 
< 0.1%
0.93 10
 
< 0.1%
0.92 7
 
< 0.1%
0.91 7
 
< 0.1%

class
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
normal
13449 
anomaly
11743 

Length

Max length7
Median length6
Mean length6.46614
Min length6

Characters and Unicode

Total characters162895
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rowanomaly
4th rownormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal 13449
53.4%
anomaly 11743
46.6%

Length

2023-04-19T18:21:26.897054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T18:21:27.133479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
normal 13449
53.4%
anomaly 11743
46.6%

Most occurring characters

ValueCountFrequency (%)
a 36935
22.7%
n 25192
15.5%
o 25192
15.5%
m 25192
15.5%
l 25192
15.5%
r 13449
 
8.3%
y 11743
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 162895
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 36935
22.7%
n 25192
15.5%
o 25192
15.5%
m 25192
15.5%
l 25192
15.5%
r 13449
 
8.3%
y 11743
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 162895
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 36935
22.7%
n 25192
15.5%
o 25192
15.5%
m 25192
15.5%
l 25192
15.5%
r 13449
 
8.3%
y 11743
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162895
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 36935
22.7%
n 25192
15.5%
o 25192
15.5%
m 25192
15.5%
l 25192
15.5%
r 13449
 
8.3%
y 11743
 
7.2%

Interactions

2023-04-19T18:21:00.563710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:37.626935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:42.553252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:48.190776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:53.534779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:58.880883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:04.257182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:09.324195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:15.079066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:20.446406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:25.830688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:31.988791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:37.152441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:42.439618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:48.624484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:53.727344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:58.690737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:03.954004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:10.006960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:15.374803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:21.223832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:26.640417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:32.320238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:37.726662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:44.666663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:50.024745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:55.154955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:21:00.789596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:37.805659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:42.949965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:48.371548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:53.725875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:59.055292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:04.433206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:09.509969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:15.258127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:20.619675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:26.595284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:32.225504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:37.325604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:42.628511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:48.803961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:53.897833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:58.864284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:04.156015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:10.185490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:15.561470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:21.406374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:26.837870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:32.512912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:37.947229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:44.858964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:50.199715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:55.328606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:21:00.997920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:38.007282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:43.168707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:48.576080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:53.942768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:59.257080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:04.640663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:09.727603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:15.472397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:20.826528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:26.819286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:32.443676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:37.525213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:42.847697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:49.009512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:54.098841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:59.113383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:04.370859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:10.423854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:15.783853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:21.617807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:27.092528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:32.745695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:38.227220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:45.071992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:50.426827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:55.531924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:21:01.192034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:38.187829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:43.369820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:48.757799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:54.136323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:59.441248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:04.825016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:09.920503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:15.663712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:21.014019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:27.021807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:32.630624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:37.720569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:43.046645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:49.195464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:54.281232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:59.309874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:04.568483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:10.608363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:15.992484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:21.809799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:27.310823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:32.949929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:38.536602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:45.281453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:50.619353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:55.704512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:21:01.398545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:38.376438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:43.584729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:48.959344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:54.377867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:59.636894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:05.019079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:10.131252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:15.876668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:21.237398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:27.229530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:32.836631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:37.934785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:43.295575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:49.405962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:54.480937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:59.510746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:04.801145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:10.828369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:16.216883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:22.037858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-04-19T18:20:33.172164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:39.799067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:45.494967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:50.822807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:55.899059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:21:01.578210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:38.542477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:43.772271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:49.135440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:54.560177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:00.142399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:05.190026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:10.319453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:16.064908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:21.411163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:27.425106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:33.016974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:38.108156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:43.485265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:49.597449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:54.661903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:59.691296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:04.999565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:11.026702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:16.415621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:22.212124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:27.681955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:33.394691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:40.034264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:45.677047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:50.994673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:56.058501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:21:01.761688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:38.715416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:43.966416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:49.318174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:54.745413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:00.321412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:05.366382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:10.509023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:16.262764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:21.597111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:27.608728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:33.197598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:38.279614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:43.688188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:49.809747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:54.841815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:59.907347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:05.178678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:11.215109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:16.613226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:22.393868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:27.869367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-04-19T18:20:08.159957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:14.147679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:19.948838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:25.254565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:31.023313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:36.576377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:43.401057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:48.745505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:54.044729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:59.303397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:21:04.850531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:41.615317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:47.200873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:52.595226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:57.881684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:03.281463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:08.382026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:14.098497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:19.415578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:24.772079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:31.028663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:36.234113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:41.341062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:47.709530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:52.823961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:57.784082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:02.936693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:08.364707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:14.360814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:20.153499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:25.502794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:31.267326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:36.755864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:43.603457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:48.936149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:54.222233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:59.502092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:21:05.036481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:41.801186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:47.402122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:52.778769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:58.082081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:03.484990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:08.580287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:14.296462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:19.619673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:24.972179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:31.222668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:36.422106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:41.539398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:47.897923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:53.002903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:57.963001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:03.134989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:08.545988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:14.557868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:20.353310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:25.794841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:31.529782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:36.943240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:43.803749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:49.125256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:54.404901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:59.709316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:21:05.226594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:42.000328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:47.601801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:52.965815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:58.284239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:03.686272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:08.771423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:14.500708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:19.846535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:25.170527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:31.418080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:36.605850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:41.723464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:48.075655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:53.179696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:58.146297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:03.346097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:09.438235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:14.754989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:20.592320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:26.052488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:31.737492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:37.158402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:43.995854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:49.336252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:54.595620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:59.916169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:21:05.423416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:42.183047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:47.797656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:53.152231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:58.483927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:03.880564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:08.953133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:14.695932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:20.049936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:25.380883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:31.611216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:36.791735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:41.928339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:48.262236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:53.362581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:58.329207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:03.555976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:09.621204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:14.960943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:20.806221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:26.240517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:31.928808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:37.348769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:44.190182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:49.574563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:54.780933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:21:00.105894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:21:05.601873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:42.370291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:47.993633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:53.343974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:18:58.679964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:04.075653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:09.133865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:14.895741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:20.246652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:25.614958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:31.795686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:36.973494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:42.184682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:48.447217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:53.545416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:19:58.508090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:03.744298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:09.816456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:15.169685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:21.005521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:26.433261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:32.116109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:37.536256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:44.455502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:49.828120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:20:54.974426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T18:21:00.333996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-04-19T18:21:27.390582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
durationsrc_bytesdst_byteshotnum_compromisednum_rootnum_file_creationsnum_access_filescountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateprotocol_typeserviceflaglandwrong_fragmenturgentnum_failed_loginslogged_inroot_shellsu_attemptednum_shellsis_guest_loginclass
duration1.0000.2200.1490.2250.1000.0540.0800.048-0.322-0.321-0.185-0.1830.0570.0520.167-0.1340.005-0.059-0.161-0.1430.1990.187-0.034-0.153-0.1530.0690.0780.0810.1580.1880.0000.0000.0000.0300.0640.1290.1700.0000.0000.080
src_bytes0.2201.0000.7000.2020.1560.0850.0770.064-0.527-0.056-0.675-0.654-0.356-0.3350.754-0.7050.288-0.4080.6190.619-0.5260.3810.347-0.625-0.610-0.225-0.2550.0000.0000.2170.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
dst_bytes0.1490.7001.0000.2000.174-0.0030.0480.073-0.441-0.020-0.536-0.510-0.294-0.2710.628-0.6040.313-0.3400.7030.667-0.6240.0610.325-0.517-0.482-0.215-0.1810.0000.1510.0510.0000.0000.0000.0000.0170.0000.0000.0000.0000.010
hot0.2250.2020.2001.0000.4990.0160.0800.017-0.150-0.141-0.088-0.083-0.0060.0180.105-0.103-0.029-0.0620.0070.064-0.061-0.006-0.068-0.064-0.0710.1020.1000.0230.3240.0330.0000.0000.0000.0000.0960.0640.0000.1350.8030.026
num_compromised0.1000.1560.1740.4991.0000.2000.1540.098-0.093-0.086-0.063-0.062-0.0020.0290.078-0.077-0.015-0.0460.0010.063-0.059-0.002-0.054-0.026-0.0230.1270.1520.0000.0390.0000.0000.0000.0000.0480.0240.5230.6800.0000.0000.014
num_root0.0540.085-0.0030.0160.2001.0000.1570.114-0.084-0.085-0.044-0.042-0.025-0.0260.051-0.047-0.029-0.054-0.030-0.0190.0350.0570.035-0.030-0.020-0.004-0.0050.0000.0390.0000.0000.0000.0000.0440.0240.5220.7070.0000.0000.014
num_file_creations0.0800.0770.0480.0800.1540.1571.0000.081-0.059-0.061-0.017-0.020-0.013-0.0150.031-0.026-0.015-0.022-0.029-0.0180.0240.014-0.0040.000-0.0060.0080.0060.0000.0530.0660.0000.0000.0000.1650.0290.0910.2650.0000.0640.016
num_access_files0.0480.0640.0730.0170.0980.1140.0811.000-0.054-0.043-0.034-0.033-0.0150.0070.040-0.0380.021-0.0040.0110.015-0.002-0.012-0.012-0.027-0.028-0.000-0.0010.0110.0300.0000.0000.0000.0000.0000.0680.4340.5030.1170.0000.044
count-0.322-0.527-0.441-0.150-0.093-0.084-0.059-0.0541.0000.5200.5790.5430.0710.064-0.7200.615-0.3210.618-0.325-0.4300.359-0.549-0.5310.5370.5070.0220.0220.2780.3810.2540.0000.0950.0000.0000.6490.0260.0000.0000.0760.651
srv_count-0.321-0.056-0.020-0.141-0.086-0.085-0.061-0.0430.5201.0000.0740.109-0.204-0.2040.030-0.0390.2350.2190.3000.255-0.242-0.199-0.1250.0480.078-0.212-0.2190.5470.3590.0800.0000.2630.0000.0000.2280.0000.0000.0000.0220.216
serror_rate-0.185-0.675-0.536-0.088-0.063-0.044-0.017-0.0340.5790.0741.0000.974-0.176-0.181-0.7580.675-0.3240.431-0.525-0.5760.487-0.488-0.3880.9340.920-0.225-0.2080.2170.2470.4050.0000.1300.0000.0000.4940.0140.0000.0000.0590.655
srv_serror_rate-0.183-0.654-0.510-0.083-0.062-0.042-0.020-0.0330.5430.1090.9741.000-0.225-0.238-0.7080.625-0.3060.412-0.479-0.5300.440-0.474-0.3680.9180.940-0.275-0.2600.2160.2380.3730.0000.0390.0000.0000.4950.0000.0000.0000.0590.653
rerror_rate0.057-0.356-0.294-0.006-0.002-0.025-0.013-0.0150.071-0.204-0.176-0.2251.0000.965-0.2190.232-0.1530.086-0.310-0.2910.289-0.012-0.084-0.184-0.2280.8420.8850.1250.1510.3760.0000.0170.0000.0080.2880.0160.0250.0000.0320.261
srv_rerror_rate0.052-0.335-0.2710.0180.029-0.026-0.0150.0070.064-0.204-0.181-0.2380.9651.000-0.2050.210-0.1210.080-0.294-0.2730.273-0.020-0.083-0.190-0.2400.8330.8940.1260.1070.3610.0000.0170.0000.0060.2920.0060.0000.0000.0280.260
same_srv_rate0.1670.7540.6280.1050.0780.0510.0310.040-0.7200.030-0.758-0.708-0.219-0.2051.000-0.9210.385-0.5400.6970.757-0.6490.5300.487-0.717-0.681-0.142-0.1550.2140.3190.2960.0000.0430.0000.0000.6050.0330.0000.0000.0690.752
diff_srv_rate-0.134-0.705-0.604-0.103-0.077-0.047-0.026-0.0380.615-0.0390.6750.6250.2320.210-0.9211.000-0.3770.523-0.668-0.7270.645-0.447-0.4790.6430.5970.1560.1590.1220.1610.1450.0000.0000.0000.0000.1710.0000.0000.0000.0080.150
srv_diff_host_rate0.0050.2880.313-0.029-0.015-0.029-0.0150.021-0.3210.235-0.324-0.306-0.153-0.1210.385-0.3771.000-0.3020.4040.444-0.4060.1560.333-0.326-0.305-0.142-0.1300.2810.2560.1090.0000.0880.0000.0000.3370.0000.0000.0000.0330.297
dst_host_count-0.059-0.408-0.340-0.062-0.046-0.054-0.022-0.0040.6180.2190.4310.4120.0860.080-0.5400.523-0.3021.000-0.353-0.5360.435-0.696-0.8350.4210.3820.0530.0350.2300.2400.1610.0100.0470.0000.0270.4630.0360.0220.0000.0720.445
dst_host_srv_count-0.1610.6190.7030.0070.001-0.030-0.0290.011-0.3250.300-0.525-0.479-0.310-0.2940.697-0.6680.404-0.3531.0000.918-0.8390.1520.451-0.527-0.471-0.265-0.2490.2590.4020.2520.0000.1190.0000.0140.6510.0000.0070.0290.1690.737
dst_host_same_srv_rate-0.1430.6190.6670.0640.063-0.019-0.0180.015-0.4300.255-0.576-0.530-0.291-0.2730.757-0.7270.444-0.5360.9181.000-0.8970.3070.538-0.582-0.517-0.249-0.2220.2240.4120.2660.0000.1420.0000.0260.6320.0350.0470.0450.2440.723
dst_host_diff_srv_rate0.199-0.526-0.624-0.061-0.0590.0350.024-0.0020.359-0.2420.4870.4400.2890.273-0.6490.645-0.4060.435-0.839-0.8971.000-0.212-0.4870.5030.4360.2690.2270.1720.2150.2140.0000.0690.0000.0280.1740.0390.0340.0000.0340.161
dst_host_same_src_port_rate0.1870.3810.061-0.006-0.0020.0570.014-0.012-0.549-0.199-0.488-0.474-0.012-0.0200.530-0.4470.156-0.6960.1520.307-0.2121.0000.561-0.459-0.4540.039-0.0070.4360.2870.1510.0200.1520.0000.0000.2090.0060.0000.0470.0350.212
dst_host_srv_diff_host_rate-0.0340.3470.325-0.068-0.0540.035-0.004-0.012-0.531-0.125-0.388-0.368-0.084-0.0830.487-0.4790.333-0.8350.4510.538-0.4870.5611.000-0.385-0.343-0.067-0.0450.4400.2960.0820.1030.0330.0000.0000.1440.0410.0470.0000.0170.170
dst_host_serror_rate-0.153-0.625-0.517-0.064-0.026-0.0300.000-0.0270.5370.0480.9340.918-0.184-0.190-0.7170.643-0.3260.421-0.527-0.5820.503-0.459-0.3851.0000.919-0.194-0.2080.2130.2500.3470.0000.0540.0000.0520.4970.0340.0350.0340.0580.659
dst_host_srv_serror_rate-0.153-0.610-0.482-0.071-0.023-0.020-0.006-0.0280.5070.0780.9200.940-0.228-0.240-0.6810.597-0.3050.382-0.471-0.5170.436-0.454-0.3430.9191.000-0.272-0.2500.2120.2590.3800.0900.0380.0000.0790.4950.0650.0800.0620.0570.656
dst_host_rerror_rate0.069-0.225-0.2150.1020.127-0.0040.008-0.0000.022-0.212-0.225-0.2750.8420.833-0.1420.156-0.1420.053-0.265-0.2490.2690.039-0.067-0.194-0.2721.0000.8840.1200.1520.3250.0000.1400.0000.0090.2770.0090.0130.0000.0290.260
dst_host_srv_rerror_rate0.078-0.255-0.1810.1000.152-0.0050.006-0.0010.022-0.219-0.208-0.2600.8850.894-0.1550.159-0.1300.035-0.249-0.2220.227-0.007-0.045-0.208-0.2500.8841.0000.1260.1570.3610.0000.0170.0000.0720.2820.0710.1060.0000.0590.293
protocol_type0.0810.0000.0000.0230.0000.0000.0000.0110.2780.5470.2170.2160.1250.1260.2140.1220.2810.2300.2590.2240.1720.4360.4400.2130.2120.1200.1261.0000.9230.2780.0000.1930.0000.0000.3850.0170.0040.0010.0450.284
service0.1580.0000.1510.3240.0390.0390.0530.0300.3810.3590.2470.2380.1510.1070.3190.1610.2560.2400.4020.4120.2150.2870.2960.2500.2590.1520.1570.9231.0000.3000.0530.2080.0000.0790.8710.1720.1370.0490.8130.859
flag0.1880.2170.0510.0330.0000.0000.0660.0000.2540.0800.4050.3730.3760.3610.2960.1450.1090.1610.2520.2660.2140.1510.0820.3470.3800.3250.3610.2780.3001.0000.0000.0520.0000.0690.6500.0530.0660.0000.0750.775
land0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0200.1030.0000.0900.0000.0000.0000.0530.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
wrong_fragment0.0000.0000.0000.0000.0000.0000.0000.0000.0950.2630.1300.0390.0170.0170.0430.0000.0880.0470.1190.1420.0690.1520.0330.0540.0380.1400.0170.1930.2080.0520.0001.0000.0000.0000.0760.0000.0000.0000.0020.101
urgent0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0800.0000.0000.0000.000
num_failed_logins0.0300.0000.0000.0000.0480.0440.1650.0000.0000.0000.0000.0000.0080.0060.0000.0000.0000.0270.0140.0260.0280.0000.0000.0520.0790.0090.0720.0000.0790.0690.0000.0000.0001.0000.0110.0000.2460.0000.0640.001
logged_in0.0640.0000.0170.0960.0240.0240.0290.0680.6490.2280.4940.4950.2880.2920.6050.1710.3370.4630.6510.6320.1740.2090.1440.4970.4950.2770.2820.3850.8710.6500.0000.0760.0000.0111.0000.0470.0350.0200.1170.688
root_shell0.1290.0000.0000.0640.5230.5220.0910.4340.0260.0000.0140.0000.0160.0060.0330.0000.0000.0360.0000.0350.0390.0060.0410.0340.0650.0090.0710.0170.1720.0530.0000.0000.0800.0000.0471.0000.5880.0250.0000.016
su_attempted0.1700.0000.0000.0000.6800.7070.2650.5030.0000.0000.0000.0000.0250.0000.0000.0000.0000.0220.0070.0470.0340.0000.0470.0350.0800.0130.1060.0040.1370.0660.0000.0000.0000.2460.0350.5881.0000.0000.0000.025
num_shells0.0000.0000.0000.1350.0000.0000.0000.1170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0450.0000.0470.0000.0340.0620.0000.0000.0010.0490.0000.0000.0000.0000.0000.0200.0250.0001.0000.0000.009
is_guest_login0.0000.0000.0000.8030.0000.0000.0640.0000.0760.0220.0590.0590.0320.0280.0690.0080.0330.0720.1690.2440.0340.0350.0170.0580.0570.0290.0590.0450.8130.0750.0000.0020.0000.0640.1170.0000.0000.0001.0000.038
class0.0800.0000.0100.0260.0140.0140.0160.0440.6510.2160.6550.6530.2610.2600.7520.1500.2970.4450.7370.7230.1610.2120.1700.6590.6560.2600.2930.2840.8590.7750.0000.1010.0000.0010.6880.0160.0250.0090.0381.000

Missing values

2023-04-19T18:21:05.997677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-19T18:21:07.005397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateclass
00tcpftp_dataSF49100000000000000000220.00.00.00.01.000.000.00150250.170.030.170.000.000.000.050.00normal
10udpotherSF146000000000000000001310.00.00.00.00.080.150.0025510.000.600.880.000.000.000.000.00normal
20tcpprivateS000000000000000000012361.01.00.00.00.050.070.00255260.100.050.000.001.001.000.000.00anomaly
30tcphttpSF23281530000010000000000550.20.20.00.01.000.000.00302551.000.000.030.040.030.010.000.01normal
40tcphttpSF199420000001000000000030320.00.00.00.01.000.000.092552551.000.000.000.000.000.000.000.00normal
50tcpprivateREJ000000000000000000121190.00.01.01.00.160.060.00255190.070.070.000.000.000.001.001.00anomaly
60tcpprivateS000000000000000000016691.01.00.00.00.050.060.0025590.040.050.000.001.001.000.000.00anomaly
70tcpprivateS0000000000000000000117161.01.00.00.00.140.060.00255150.060.070.000.001.001.000.000.00anomaly
80tcpremote_jobS0000000000000000000270231.01.00.00.00.090.050.00255230.090.050.000.001.001.000.000.00anomaly
90tcpprivateS000000000000000000013381.01.00.00.00.060.060.00255130.050.060.000.001.001.000.000.00anomaly
durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateclass
251820tcpotherREJ00000000000000000051110.120.000.851.00.001.000.0025510.001.000.000.000.160.00.821.0anomaly
251830tcpprivateREJ00000000000000000031410.030.000.951.00.001.000.0025510.001.000.000.000.040.00.961.0anomaly
2518429tcpftpSF32910630006010000000001110.000.000.000.01.000.000.00255600.240.020.000.000.000.00.030.1normal
251851tcpsmtpSF28963330000010000000000130.000.000.000.01.000.001.0012110.920.170.080.000.000.00.000.0normal
251860tcphttpS13391460000000100000000002330.500.030.000.01.000.000.061732551.000.000.010.010.010.00.010.0normal
251870tcpexecRSTO00000000000000000010070.000.001.001.00.070.070.0025570.030.060.000.000.000.01.001.0anomaly
251880tcpftp_dataSF33400000010000000000110.000.000.000.01.000.000.001391.000.001.000.180.000.00.000.0anomaly
251890tcpprivateREJ00000000000000000010570.000.001.001.00.070.070.00255130.050.070.000.000.000.01.001.0anomaly
251900tcpnnspS0000000000000000000129181.001.000.000.00.140.060.00255200.080.060.000.001.001.00.000.0anomaly
251910tcpfingerS00000000000000000003891.001.000.000.00.240.110.00255490.190.030.010.001.001.00.000.0anomaly